predict

Predict next observation, next reward, or episode termination given observation and action input data

Since R2022a

Syntax

predNextObs = predict(tsnFcnAppx,obs,act)

predReward = predict(rwdFcnAppx,obs,act,nextObs)

predIsDone = predict(idnFcnAppx,obs,act)

___ = predict(___,UseForward=useForward)

Description

predNextObs = predict(tsnFcnAppx,obs,act) evaluates the environment transition function approximator object tsnFcnAppx and returns the predicted next observation nextObs, given the current observation obs and the action act.

predReward = predict(rwdFcnAppx,obs,act,nextObs) evaluates the environment reward function approximator object rwdFcnAppx and returns the predicted reward predReward, given the current observation obs, the action act, and the next observation nextObs.

predIsDone = predict(idnFcnAppx,obs,act) evaluates the environment is-done function approximator object idnFcnAppx and returns the predicted is-done status predIsDone, given the current observation obs, the action act, and the next observation nextObs.

___ = predict(___,UseForward=useForward) allows you to explicitly call a forward pass when computing gradients.

Examples

Predict Next Observation Using Continuous Gaussian Transition Function Approximator

Create observation and action specification objects (or alternatively use getObservationInfo and getActionInfo to extract the specification objects from an environment). For this example, two observation channels carry vectors in a four- and two-dimensional space, respectively. The action is a continuous three-dimensional vector.

obsInfo = [
    rlNumericSpec([4 1],UpperLimit=10*ones(4,1));
    rlNumericSpec([1 2],UpperLimit=20*ones(1,2))
    ];

actInfo = rlNumericSpec([3 1]);

Create a deep neural network to use as approximation model for the transition function approximator. For a continuous Gaussian transition function approximator, the network must have two output layers for each observation (one for the mean values the other for the standard deviation values).

Define each network path as an array of layer objects. Get the dimensions of the observation and action spaces from the environment specification objects, and specify a name for the input layers, so you can later explicitly associate them with the appropriate environment channel.

% Input path layers from first observation channel
obs1Path = [ 
    featureInputLayer( ...
        prod(obsInfo(1).Dimension), ...
        Name="obs1InLyr")
    fullyConnectedLayer(5,Name="obs1PathOutLyr")
    ];

% Input path layers from second observation channel
obs2Path = [ 
    featureInputLayer( ...
        prod(obsInfo(2).Dimension), ...
        Name="obs2InLyr")
    fullyConnectedLayer(5,Name="obs2PathOutLyr") 
    ];

% Input path layers from action channel
actPath = [ 
    featureInputLayer( ...
         prod(actInfo(1).Dimension), ...
         Name="actInLyr")
    fullyConnectedLayer(5,Name="actPathOutLyr") 
    ];

% Joint path layers, concatenate 3 inputs along first dimension
jointPath = [ 
    concatenationLayer(1,3,Name="concat")
    tanhLayer(Name="jntPathTahnLyr");
    fullyConnectedLayer(10,Name="jntfc") 
    ];

% Path layers for mean values of first predicted obs
% Using tanh and scaling layers to scale range from (-1,1) to (-10,10)
% Note that scale vector must be a column vector
mean1Path = [ 
    fullyConnectedLayer(prod(obsInfo(1).Dimension), ...
        Name="mean1PathInLyr");
    tanhLayer(Name="mean1PathTahnLyr");
    scalingLayer(Name="mean1OutLyr", ...
            Scale=obsInfo(1).UpperLimit) 
    ];

% Path layers for standard deviations first predicted obs
% Using softplus layer to make them non negative
std1Path = [ 
    fullyConnectedLayer(prod(obsInfo(1).Dimension), ...
        Name="std1PathInLyr");
    softplusLayer(Name="std1OutLyr") 
    ];

% Path layers for mean values of second predicted obs
% Using tanh and scaling layers to scale range from (-1,1) to (-20,20)
% Note that scale vector must be a column vector
mean2Path = [ 
    fullyConnectedLayer(prod(obsInfo(2).Dimension), ...
        Name="mean2PathInLyr");
    tanhLayer(Name="mean2PathTahnLyr");
    scalingLayer(Name="mean2OutLyr", ...
        Scale=obsInfo(2).UpperLimit(:)) 
    ];

% Path layers for standard deviations second predicted obs
% Using softplus layer to make them non negative
std2Path = [ 
    fullyConnectedLayer(prod(obsInfo(2).Dimension), ...
        Name="std2PathInLyr");
    softplusLayer(Name="std2OutLyr") 
    ];

% Assemble dlnetwork object.
net = dlnetwork;
net = addLayers(net,obs1Path);
net = addLayers(net,obs2Path);
net = addLayers(net,actPath);
net = addLayers(net,jointPath);
net = addLayers(net,mean1Path);
net = addLayers(net,std1Path);
net = addLayers(net,mean2Path);
net = addLayers(net,std2Path);

% Connect layers.
net = connectLayers(net,"obs1PathOutLyr","concat/in1");
net = connectLayers(net,"obs2PathOutLyr","concat/in2");
net = connectLayers(net,"actPathOutLyr","concat/in3");
net = connectLayers(net,"jntfc","mean1PathInLyr/in");
net = connectLayers(net,"jntfc","std1PathInLyr/in");
net = connectLayers(net,"jntfc","mean2PathInLyr/in");
net = connectLayers(net,"jntfc","std2PathInLyr/in");

% Plot network.
plot(net)

% Initialize network.
net = initialize(net);

% Display the number of weights.
summary(net)

   Initialized: true

   Number of learnables: 352

   Inputs:
      1   'obs1InLyr'   4 features
      2   'obs2InLyr'   2 features
      3   'actInLyr'    3 features

Create a continuous Gaussian transition function approximator object, specifying the names of all the input and output layers.

tsnFcnAppx = rlContinuousGaussianTransitionFunction(...
    net,obsInfo,actInfo,...
    ObservationInputNames=["obs1InLyr","obs2InLyr"], ...
    ActionInputNames="actInLyr", ...
    NextObservationMeanOutputNames= ...
        ["mean1OutLyr","mean2OutLyr"], ...
    NextObservationStandardDeviationOutputNames= ...
        ["std1OutLyr","std2OutLyr"] );

Predict the next observation for a random observation and action.

predObs = predict(tsnFcnAppx, ...
    {rand(obsInfo(1).Dimension),rand(obsInfo(2).Dimension)}, ...
    {rand(actInfo(1).Dimension)})

predObs=1×2 cell array
    {4x1 single}    {[-19.2685 -1.1779]}

Each element of the resulting cell array represents the prediction for the corresponding observation channel.

To display the mean values and standard deviations of the Gaussian probability distribution for the predicted observations, use evaluate.

predDst = evaluate(tsnFcnAppx, ...
    {rand(obsInfo(1).Dimension), ...
     rand(obsInfo(2).Dimension), ...
     rand(actInfo(1).Dimension)})

predDst=1×4 cell array
    {4x1 single}    {[-16.6873 4.4006]}    {4x1 single}    {[0.7455 1.4318]}

The result is a cell array in which the first and second element represent the mean values for the predicted observations in the first and second channel, respectively. The third and fourth element represent the standard deviations for the predicted observations in the first and second channel, respectively.

Create Deterministic Reward Function and Predict Reward

Create an environment interface and extract observation and action specifications. Alternatively, you can create specifications using rlNumericSpec and rlFiniteSetSpec.

env = rlPredefinedEnv("CartPole-Continuous");
obsInfo = getObservationInfo(env);
actInfo = getActionInfo(env);

To approximate the reward function, create a deep neural network. For this example, the network has two input layers, one for the current action and one for the next observations. The single output layer contains a scalar, which represents the value of the predicted reward.

Define each network path as an array of layer objects. Get the dimensions of the observation and action spaces from the environment specifications, and specify a name for the input layers, so you can later explicitly associate them with the appropriate environment channel.

actionPath = featureInputLayer( ...
    actInfo.Dimension(1), ...
    Name="action");

nextStatePath = featureInputLayer( ...
    obsInfo.Dimension(1), ...
    Name="nextState");

commonPath = [concatenationLayer(1,2,Name="concat")
    fullyConnectedLayer(64)
    reluLayer
    fullyConnectedLayer(64)
    reluLayer
    fullyConnectedLayer(64)
    reluLayer
    fullyConnectedLayer(1)];

Assemble dlnetwork object.

net = dlnetwork();
net = addLayers(net,nextStatePath);
net = addLayers(net,actionPath);
net = addLayers(net,commonPath);

Connect layers.

net = connectLayers(net,"nextState","concat/in1");
net = connectLayers(net,"action","concat/in2");

Plot network.

plot(net)

Initialize network and display the number of weights.

net = initialize(net);
summary(net)

   Initialized: true

   Number of learnables: 8.7k

   Inputs:
      1   'nextState'   4 features
      2   'action'      1 features

Create a deterministic transition function object.

rwdFcnAppx = rlContinuousDeterministicRewardFunction(...
    net,obsInfo,actInfo,...
    ActionInputNames="action", ...
    NextObservationInputNames="nextState");

Using this reward function object, you can predict the next reward value based on the current action and next observation. For example, predict the reward for a random action and next observation. Since, for this example, only the action and the next observation influence the reward, use an empty cell array for the current observation.

act = rand(actInfo.Dimension);
nxtobs = rand(obsInfo.Dimension);
reward = predict(rwdFcnAppx, {}, {act}, {nxtobs})

reward = single

0.1034

To predict the reward, you can also use evaluate.

reward_ev = evaluate(rwdFcnAppx, {act,nxtobs} )

reward_ev = 1x1 cell array
    {[0.1034]}

Create Is-Done Function and Predict Termination

Create an environment interface and extract observation and action specifications. Alternatively, you can create specifications using rlNumericSpec and rlFiniteSetSpec.

env = rlPredefinedEnv("CartPole-Continuous");
obsInfo = getObservationInfo(env);
actInfo = getActionInfo(env);

To approximate the is-done function, use a deep neural network. The network has one input channel for the next observations. The single output channel is for the predicted termination signal.

Create the neural network as a vector of layer objects.

net = [
    featureInputLayer( ...
                obsInfo.Dimension(1), ...
                Name="nextState")
    fullyConnectedLayer(64)
    reluLayer
    fullyConnectedLayer(64)
    reluLayer
    fullyConnectedLayer(2)
    softmaxLayer(Name="isdone")
    ];

Convert to dlnetwork object.

net = dlnetwork(net);

Plot network.

plot(net)

Initialize network and display the number of weights.

net = initialize(net);
summary(net);

   Initialized: true

   Number of learnables: 4.6k

   Inputs:
      1   'nextState'   4 features

Create an is-done function approximator object.

isDoneFcnAppx = rlIsDoneFunction(...
    net,obsInfo,actInfo,...
    NextObservationInputNames="nextState");

Using this is-done function approximator object, you can predict the termination signal based on the next observation. For example, predict the termination signal for a random next observation. Since for this example the termination signal only depends on the next observation, use empty cell arrays for the current action and observation inputs.

nxtobs = rand(obsInfo.Dimension);
predIsDone = predict(isDoneFcnAppx,{},{},{nxtobs})

predIsDone = 
0

You can obtain the termination probability using evaluate.

predIsDoneProb = evaluate(isDoneFcnAppx,{nxtobs})

predIsDoneProb = 1x1 cell array
    {2x1 single}

predIsDoneProb{1}

ans = 2x1 single column vector

    0.5405
    0.4595

The first number is the probability of obtaining a 0 (no termination predicted), the second one is the probability of obtaining a 1 (termination predicted).

Input Arguments

tsnFcnAppx — Environment transition function approximator object

rlContinuousDeterministicTransitionFunction object | rlContinuousGaussianTransitionFunction object

Environment transition function approximator object, specified as one of the following:

• rlContinuousDeterministicTransitionFunction object

• rlContinuousGaussianTransitionFunction object

rwdFcnAppx — Environment reward function

rlContinuousDeterministicRewardFunction object | rlContinuousGaussianRewardFunction object | function handle

Environment reward function approximator object, specified as one of the following:

• rlContinuousDeterministicRewardFunction object

• rlContinuousGaussianRewardFunction object

• Function handle object. For more information about function handle objects, see What Is a Function Handle?.

idnFcnAppx — Environment is-done function approximator object

rlIsDoneFunction object

Environment is-done function approximator object, specified as an rlIsDoneFunction object.

obs — Observations

cell array

Observations, specified as a cell array with as many elements as there are observation input channels. Each element of obs contains an array of observations for a single observation input channel.

The dimensions of each element in obs are M_O-by-L_B, where:

• M_O corresponds to the dimensions of the associated observation input channel.

• L_B is the batch size. To specify a single observation, set L_B = 1. To specify a batch of observations, specify L_B > 1. If valueRep or qValueRep has multiple observation input channels, then L_B must be the same for all elements of obs.

L_B must be the same for both act and obs.

For more information on input and output formats for recurrent neural networks, see the Algorithms section of lstmLayer.

act — Action

single-element cell array

Action, specified as a single-element cell array that contains an array of action values.

The dimensions of this array are M_A-by-L_B, where:

• M_A corresponds to the dimensions of the associated action specification.

• L_B is the batch size. To specify a single observation, set L_B = 1. To specify a batch of observations, specify L_B > 1.

L_B must be the same for both act and obs.

For more information on input and output formats for recurrent neural networks, see the Algorithms section of lstmLayer.

nextObs — Next observation

cell array

Next observations, that is the observation following the action act from the observation obs, specified as a cell array of the same dimension as obs.

useForward — Option to use parallel training

false (default) | true

Option to use forward pass, specified as a logical value. When you specify UseForward=true the function calculates its outputs using forward instead of predict. This allows layers such as batch normalization and dropout to appropriately change their behavior for training.

Example: true

Output Arguments

predNextObs — Predicted next observation

cell array

Predicted next observation, that is the observation predicted by the transition function approximator tsnFcnAppx given the current observation obs and the action act, returned as a cell array of the same dimension as obs.

predReward — Predicted next observation

single

Predicted reward, that is the reward predicted by the reward function approximator rwdFcnAppx given the current observation obs, the action act, and the following observation nextObs, returned as a single.

predIsDone — Predicted next observation

double

Predicted is-done episode status, that is the episode termination status predicted by the is-done function approximator rwdFcnAppx given the current observation obs, the action act, and the following observation nextObs, returned as a double.

Note

If fcnAppx is an rlContinuousDeterministicRewardFunction object, then evaluate behaves identically to predict except that it returns results inside a single-cell array. If fcnAppx is an rlContinuousDeterministicTransitionFunction object, then evaluate behaves identically to predict. If fcnAppx is an rlContinuousGaussianTransitionFunction object, then evaluate returns the mean value and standard deviation the observation probability distribution, while predict returns an observation sampled from this distribution. Similarly, for an rlContinuousGaussianRewardFunction object, evaluate returns the mean value and standard deviation the reward probability distribution, while predict returns a reward sampled from this distribution. Finally, if fcnAppx is an rlIsDoneFunction object, then evaluate returns the probabilities of the termination status being false or true, respectively, while predict returns a predicted termination status sampled with these probabilities.

Tips

When the elements of the cell array in inData are dlarray objects, the elements of the cell array returned in outData are also dlarray objects. This allows predict to be used with automatic differentiation.

Specifically, you can write a custom loss function that directly uses predict and dlgradient within it, and then use dlfeval and dlaccelerate with your custom loss function. For an example, see Train Reinforcement Learning Policy Using Custom Training Loop and Custom Training Loop with Simulink Action Noise.

Version History

Introduced in R2022a